## AI applied estimation of learning ρ method

2018/10/01 Yasunori Ushiro (Kanagawa University)

### 1. Features of learning ρ method

(1) Discovery of learning ability

Discover fixed method for trajectory group of ρ method

(2) Relationship between learning volume and decoding time

The decoding speed of elliptic curve cryptography improves in proportion to the amount of learning.

(3) Evolution possibility

Application of AI is expected from the use of arbitrary points and fixation of trajectory group

### 2. Goal of AI application

(1) Initial application of AI

100,000 times faster than ρ method. Three years later (2021)

It is 10,000 times by the learning ρ method. 10 times faster with AI.

(2) AI intermediate period

100,000,000 times faster than ρ method. 6 years later (2024)

It is 100,000 times by the learning ρ method. 1,000 times faster with AI.

Current elliptic curve cryptography is yellow signal.

(3) AI application practical period

1000,000,000,000 times faster than ρ method. 10 years later (2028)

It is 1,000,000 times by the learning ρ method. 1,000,000 times faster with AI.

Current elliptic curve cryptography is red signal.

### 3. Destructive power estimation of the United States (NSA)

Prediction from the discovery ρ method discovery history and NSA cryptographic capability.

(1) Estimated present (2018)

Learnig ρ:80%, case-(1):50%, case-(2)：30%, case-(3):10%

(2) Estimated five years later (2023)

Learnig ρ:100%, case-(1):95%, case-(2):80%, case-(3):50%